In a fabrication process of semiconductors, it is important to clear up the cause of occurrence of a defect on a semiconductor wafer in order to improve the yield. In the existing circumstances, a defect inspection device and a defect observation device are used to analyze a defect in the semiconductor fabrication field.
The defect inspection device employs optical means or an electron beam to observe a wafer and produces positional coordinates of a detected defect. Since it is important for the defect inspection device to make processing over the wide range at high speed, the data amount of image to be obtained is reduced by increasing the pixel size of the image (that is, low resolution) as large as possible. In many cases, even if existence of a defect can be confirmed from the detected image of low resolution, it is difficult to identify a kind of the defect (defect type) in detail.
Accordingly, the defect observation device is used for identification of the defect type. The defect observation device employs output information of the defect inspection device to photograph defect coordinates of wafer with high resolution and produce an image or picture. Miniaturization of the fabrication process of semiconductor devices is advanced, so that the size of defect also reaches the several nm range with the miniaturization and the resolution of several nm range is required in order to observe the defect in detail.
Therefore, in recent years, the defect observation device (review SEM) using a scanning electron microscope (SEM) is employed widely. The review SEM has the function of automatic defect review (ADR) for automatically collecting high-resolution images of defects (defect images) on a wafer by using the defect coordinates produced by the defect inspection device.
In recent years, the throughput of ADR of the review SEM is improved and accordingly it is desired that operation of identifying the defect type from a large amount of defect images collected is automatized. The review SEM has the function of automatic defect classification (ADC) for automatically identifying the defect type from the defect images to be classified.
As a method of automatically classifying the defect images for each defect type, Patent Literature 1 describes a method of processing the defect images to quantify the feature amount of external appearance of the defect part and classify defects using a neural network, for example. Further, as a method of being capable of easily coping with even the case where there are many kinds of defects (defect types) to be classified, Patent Literature 2 describes a method of classifying the defects by combining a rule base classification method with an instruction classification method, for example.
In the automatic classification of defect images, classification is performed on the basis of classification recipes. The classification recipes contain various parameters such as image processing parameters, information of the defect types to be classified (classification classes), defect images belonging to the classification classes (instruction pictures) and the like. When a new defect type is produced due to variation in the fabrication process, a classification class of the new defect is added in the classification recipes to be updated. Patent Literature 3 describes a method in which when the defect images are automatically classified, a new defect is judged as a defect of which the classification class is not clear (unknown defect) and a new classification class is added to the classification recipes to be updated. Further, the unknown defect contains a defect which occurs due to instruction error by the user and exists beyond the classification class defined in the classification recipes.
Heretofore, there are circumstances in which classification of defect images is manually performed by a person before the defect observation device and accordingly the defect observation device generally has the automatic classification function of defect images as part of the function thereof. However, with increase of production quantities of semiconductor products, a plurality of defect observation devices are introduced in the fabrication line of semiconductor wafers and there arises a problem that a cost for management of the classification recipes is increased. As opposed to this problem, Patent Literature 4 describes a method in which a plurality of image detection devices are connected to an information processing device through a network and photographed images are transferred to the image processing device so that the image processing device judges whether anything unusual appears in the images or not.